Analysis of Chinese economic growth under technology and consumer components of terms of trade: a time series study (1980–2021)

The existing literature widely explores the relationship between international trade and economic development. This research provides an innovative contribution by examining the impact of terms of trade (TOT), labor force, gross capital, stock trade, and investment on China’s economic growth from 1980 to 2022, employing the Autoregressive Distributed Lag (ARDL) long and short-run models. Furthermore, we divide terms of trade into consumer goods components (TOT food and TOT goods) and technological components (TOT computer & communication and TOT fuel). The outcomes of the Bound and Co-integration tests support the suitability of a long-run ARDL model, revealing a positive relationship between terms of trade and Chinese economic growth. Among the TOT components, TOT food and TOT goods promote Chinese economic growth, while TOT computer & communication and TOT fuel have an adverse effect. Additionally, investment, gross capital, stock trade, and labor force significantly boost China’s economic growth. However, the ECM (-1) outcomes indicate a 2098.48% adjustment from disequilibrium to equilibrium in transitioning from the short-run to the long-run. The study emphasizes the significance of export-oriented strategies, particularly technology, goods, and food exports, while addressing challenges like fuel dependency and global price volatility in fostering sustainable economic development.

Authors

Institutions

Publication Details

Journal
Discover Sustainability
Published
2025-11-21
DOI
https://doi.org/10.1007/s43621-025-01669-w
Citations
1
Primary Topic
Energy, Environment, Economic Growth
Type
article
Field-Weighted Citation Impact
3.13
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Analysis of Chinese economic growth under technology and consumer components of terms of trade: a time series study (1980–2021)

Umer Shahzad, Changhong Miao
1 citations
Discover Sustainability
Energy, Environment, Economic Growth
3.13
article

Analysis of Chinese economic growth under technology and consumer components of terms of trade: a time series study (1980–2021)

Umer Shahzad, Changhong Miao
article en
1 citations

Abstract

The existing literature widely explores the relationship between international trade and economic development. This research provides an innovative contribution by examining the impact of terms of trade (TOT), labor force, gross capital, stock trade, and investment on China’s economic growth from 1980 to 2022, employing the Autoregressive Distributed Lag (ARDL) long and short-run models. Furthermore, we divide terms of trade into consumer goods components (TOT food and TOT goods) and technological components (TOT computer & communication and TOT fuel). The outcomes of the Bound and Co-integration tests support the suitability of a long-run ARDL model, revealing a positive relationship between terms of trade and Chinese economic growth. Among the TOT components, TOT food and TOT goods promote Chinese economic growth, while TOT computer & communication and TOT fuel have an adverse effect. Additionally, investment, gross capital, stock trade, and labor force significantly boost China’s economic growth. However, the ECM (-1) outcomes indicate a 2098.48% adjustment from disequilibrium to equilibrium in transitioning from the short-run to the long-run. The study emphasizes the significance of export-oriented strategies, particularly technology, goods, and food exports, while addressing challenges like fuel dependency and global price volatility in fostering sustainable economic development.

Discover SustainabilityVol. 6(1)
Henan University (CN)
Openalex Percentile: Top 6%
Energy, Environment, Economic Growth
3.13
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.